This invention relates to a data acquisition system suitable for a production facility of the kind including a large number of individual work stations through which a multiplicity of workpieces progress in the fabrication of a single article. The invention is described in the environment of a sewing plant for garment manufacture; however, the system is also applicable to other production facilities having similar general characteristics.
The manufacture of a garment usually entails many individual production operations, from cutting of the cloth to final pressing of the completed garment. For example, the manufacture of a suit jacket or sports jacket may require a total of one hundred twenty or more separate steps, including cutting, fusing, sewing, pressing, and other operations. For different styles, the number of operations may vary substantially. Thus, one jacket style may have patch pockets and another may use pocket flaps; one style can include three buttons on each sleeve, another may have two buttons per sleeve, and another may have none. There is, quite literally, no consistent, standardized set of operations. Nevertheless, a garment factory producing garments subject to style and fashion variations must provide for concurrent manufacture of a wide variety of styles, the work content of the shop shifting constantly on a day-to-day basis.
Intelligent scheduling of production in a garment sewing shop requires the compilation of a great deal of information. For effective and efficient production scheduling, management should know which operators are present and available, the skills of those operators, current production bottlenecks and the likelihood of impending bottlenecks. Of course, it is essential to know the production peculiarities of specific garment styles and the permissible trade-offs between alternative items. In some manner, data regarding all of these disparate factors should be collected and organized to allow for effective scheduling decisions, particularly in an era in which style and fashion concepts are subject to rapid and repeated change.
Traditional techniques for obtaining basic data regarding operators and their skills are essentially byproducts of incentive payroll reporting schemes. For each garment, a document known as a bundle ticket is prepared; the bundle ticket identifies the cut, style, and component requirements of the garment. The bundle ticket includes a series of coupons, one for each individual operation to be performed in manufacture of the garment. There are also a series of additional similar bundle tickets for subassemblies incorporated in the complete garment. For example, for a jacket, in addition to the basic bundle ticket for the overall garment there may be separate tickets, with individual coupons, for the front, back, collar, sleeves, pocket flaps, pocket patches, yoke, and canvas portions of the garment. As each operation is performed, the related coupon for that operation is cut from the ticket and is used to record the work done and the operator identity for payroll purposes. Traditionally, the bundle tickets and coupons are the only records available to track the progress of the garment components through the shop.
A coupon-based system, in which the individual operators control the return flow of coupons, is inherently unreliable as a source of accurate production statistics. These and other known payroll reporting techniques fall far short of accurately reporting data adequate for determination of individual productivity by style, lost time, increasing or decreasing operator efficiency for new models, etc. Furthermore, the traditional recording techniques do not provide additional data essential to effective production scheduling, such as the operational status of the production equipment, the distribution of work-in-process inventory among the many operations in the plant, and the work requirements, by operation, of individual styles in process.
A production manager would be swamped in any attempt to contend manually with such an immense volume of specific detail. Some improvement is made possible by effective use of a computer in collating and summarizing the data available from a conventional coupon system or other similar systems. In general, however, the production manager of a garment sewing shop has little direct knowledge of the status of any group of garment components after it enters the shop and prior to the time the completed garments emerge. Production scheduling, therefore, is based in large part on intuition and experience.
An intelligent substitute for experience in a high-production variable-style garment shop would be a sophisticated mathematical model simulating the complete garment manufacturing process. Such a model can afford an effective guide to management in utilizing plant resources efficiently to smooth the flow of production and alleviate impending bottlenecks. Building a computer model of a style shop comprising scores of operations, hundreds of operators, and thousands of units in inventory, however, can be a truly awesome task. Only by capturing data as each operation occurs on each garment in the sewing shop can the computer model hope to achieve the precision required for effective analysis and reliable predictions.
In a sewing shop, manufacturing operations are typically of short duration and are performed by sedentary operators working on relatively low-cost equipment. Consequently, it is impractical and economically infeasible to monitor these activities with conventional data collection devices, which in many instances have a cost comparable to that of the manufacturing equipment at each work station. Thus, for an effective data acquisition system applicable to a garment sewing shop or similar production facility, low cost for each work station terminal is of critical importance. Furthermore, it is not desirable to have a single terminal serve multiple work stations because this requires the operators to leave their stations periodically to enter information in the multi-station terminal, creating unprofitable work flow interruptions and adding materially to operational costs. Thus, there is a basic need for a data acquisition terminal having a cost substantially lower than the cost of production equipment at each work station.
The requirements for source data collection in a sewing shop are quite stringent in other respects as well. To begin with, each operation should be logged as it occurs, avoiding the time delays introduced by operator reporting activities in coupon systems. A data acquisition system that signals both the start and the end of each production operation is also highly desirable, because it affords a readout of non-productive time between operations as well as productive work time.
Each production operation should also be identified completely according to style, workpieces being processed, the work performed, and operator identity. This level of detail is desirable not only as input to an effective computer model of the shop but also for subsequent production analyses. Moreover, events relating to the work status of an operator, customarily captured on time cards and work tickets, should be collected as they occur to account for the activities of each operator for payroll purposes. Finally, because sewing operators cannot be expected to possess high-level clerical skills, it is most desirable to eliminate any necessity for entry of data through the use of a keyboard or any like input device.
Another factor of substantial practical importance is adaptability to relocation. Shifting fashion trends frequently lead to work station realignments; the data acquisition system should provide for terminal relocation anyplace in the shop with minimum bother. A simple plug-it-in arrangement is most desirable.
A data acquisition system having the general characteristics noted above can provide other desirable attributes as well, particularly if each work station terminal includes a keyboard for entry of specialized information, normally not utilized for the basic data. Thus, machine breakdown and repair may be logged by supervisory personnel for use in scheduling maintenance, estimating costs of further mechanization, and tracking productivity of mechanics. Other specialized data may include authorization for an operator to work overtime or to leave the work station before the end of a shift, identification of work on a new style for which an operator is to be paid on an hourly basis rather than an incentive basis, and occurrence of an interruption in workflow to a station without fault of the operator requiring compensation on a time basis.
Thus, a data acquisition system to be used in production scheduling and general control of a garment sewing shop or like production facility should compile accurate real-time data relating to all of the disparate factors affecting production. This information should be captured at its sources, the individual work stations, through low cost equipment that is "intelligent" enough to compile all basic data through scanning of simple identification members so that no clerical skill or training is required of individual operators.
Previously proposed factory data collection systems have not met the criteria set forth above. Thus, such systems have usually employed keyboards, sometimes in combination with conventional punched card readers, bar code scanners, or like devices, for basic data entry. Previously known systems have cuustomarily used dedicated multi-pair wiring, telephone lines, or even coaxial cable for communication between data entry points and central data compilation facilities, making relocation of data entry terminals both difficult and expensive. The system terminals are generally too expensive to place at every work station. The results are not satisfactory in garment shops and other manufacturing facilities characterized by sedentary operators, low work place investment, short cycle times, and frequent physical layout revisions. Furthermore, previously known systems have not been readily adaptable to changes in data input devices and techniques (e.g. punched cards to bar code ID members) or to acquisition of disparate kinds of data (e.g. process parameters) conjointly with basic production data.